Field of the Invention
The present disclosure generally relates to an object indexing method, particularly to an object indexing method for indexing an object into a database rapidly. The present disclosure also relates to an object searching method and an object indexing system which apply said object indexing method.
Description of the Related Art
Currently there exist a number of object indexing and searching applications in which an object is for example a human face, a specific text or audio. For example, video surveillance is used to capture and record videos of a number of public, private locations such as airports, railway stations, supermarkets, houses and other locations with the presence of human, cars, etc. The surveillance cameras will capture and record lots of objects into database for long period of time, called an indexing process, so that the past presence of human or cars, etc. can be retrieved and inspected, called a searching process. However the manual check of large size of video data can be extremely labor intensive and time consuming. The manual checking method is not suitable for many scenarios which require retrieving in real time. For example, parents need to find their separating children in airport as quickly as possible avoiding delay an international trip. In such a case, human image retrieving algorithms in real time have been developed such that a target object can be indexed and retrieved in a short time period.
Conventional human image retrieve approaches at least include two processes of image indexing and image searching. The image indexing process includes features calculation, feature based clustering, and clustering based classification. The corresponding image searching process includes similarity calculation of comparing a querying image to the value of cluster centers, cluster identification of identifying clusters with the minimal distance, and image retrieving of retrieving similar images from the identified clusters.
For video surveillance applications, fast indexing is needed so that we can search for wanted human in real time. For fast indexing, there may be many human images in one second. The total indexing time of the above images should be shorter than one second. Otherwise the fast indexing system will be blocked, and the delay time for indexing images will become longer and longer.
In a Japanese patent application JP05155025, a cluster based similar image search approach is described for human image indexing. First, an image is classified into nearest cluster. Then, when the size of the cluster is larger than a threshold value, all nodes of this cluster and nearby clusters are read into a memory to calculate the energy and then split it.
There are some issues with this approach. Although the complex constructing process for registering bulk images can improve accuracy in search, the register time is long and the delay is long. When reaching split or reconstruct condition, CPU will be very busy in calculation and large amount of disk I/O is needed and thus the system cannot respond to any new register image with the result of a low capacity of load. We should avoid this case during fast indexing, especially when large amount of human keep coming in a short period of time. On the other hand, in order to synchronize indexed data for search, transaction should be used. So the delay time is long. If using fast indexing for registering image one by one can reduce the delay time, then the accuracy in search is very low. When the fast indexing process uses category classifying, it is difficult to control the member size for each category. Maybe all images during this period are classified into the same category. At this situation, the search speed becomes slow and this approach is similar to a scan method.